27 Pages Posted: 18 Mar 2017
Date Written: March 16, 2017
Bayesian experts with a common prior that are exposed to different types of evidence possibly make contradicting probabilistic forecasts. A policy maker who receives the forecasts must aggregate them in the best way possible. This is a challenge whenever the policy maker is not familiar with the prior nor the model and evidence available to the experts. We propose a model of non-Bayesian forecast aggregation and adapt the notion of regret as a means for evaluating the policy maker's performance. Whenever experts are Blackwell ordered taking a weighted average of the two forecasts, the weight of which is proportional to its precision (the reciprocal of the variance), is optimal. The resulting regret is equal 1/8(5*sqrt(5)-11)≈0.0225425, which is 3 to 4 times better than naive approaches such as choosing one expert at random or taking the non-weighted average.
Keywords: Forecast Aggregation, Aggregation Scheme
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